
import pandas as pd
import numpy as np


pd.read_csv("tdata.csv",nrows = 10)


pd.read_csv("tdata.csv",nrows = 10).info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 10 entries, 0 to 9
Data columns (total 21 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   Trip ID                     10 non-null     object 
 1   Trip Start Timestamp        10 non-null     object 
 2   Trip End Timestamp          10 non-null     object 
 3   Trip Seconds                10 non-null     int64  
 4   Trip Miles                  10 non-null     float64
 5   Pickup Census Tract         1 non-null      float64
 6   Dropoff Census Tract        3 non-null      float64
 7   Pickup Community Area       8 non-null      float64
 8   Dropoff Community Area      9 non-null      float64
 9   Fare                        10 non-null     float64
 10  Tip                         10 non-null     int64  
 11  Additional Charges          10 non-null     float64
 12  Trip Total                  10 non-null     float64
 13  Shared Trip Authorized      10 non-null     bool   
 14  Trips Pooled                10 non-null     int64  
 15  Pickup Centroid Latitude    8 non-null      float64
 16  Pickup Centroid Longitude   8 non-null      float64
 17  Pickup Centroid Location    8 non-null      object 
 18  Dropoff Centroid Latitude   9 non-null      float64
 19  Dropoff Centroid Longitude  9 non-null      float64
 20  Dropoff Centroid Location   9 non-null      object 
dtypes: bool(1), float64(12), int64(3), object(5)
memory usage: 1.7+ KB


year_data = pd.read_csv("tdata.csv", usecols={"Pickup Census Tract","Trip Miles","Trip Seconds"})


year_data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 49871385 entries, 0 to 49871384
Data columns (total 3 columns):
 #   Column               Dtype  
---  ------               -----  
 0   Trip Seconds         float64
 1   Trip Miles           float64
 2   Pickup Census Tract  float64
dtypes: float64(3)
memory usage: 1.1 GB


year_data.head(10)


year_data = year_data.rename(columns={"Trip Seconds":"Trip_Seconds","Trip Miles":"Trip_Miles","Pickup Census Tract":"GEOID"})


year_data = year_data.dropna()


year_data


year_data = year_data.astype({"GEOID":int})
year_data.head()


year_data = year_data[["GEOID","Trip_Miles","Trip_Seconds"]]
year_data.head()


year_data.to_csv("geoid-miles-seconds.csv")


year_data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 25487623 entries, 5 to 49871384
Data columns (total 3 columns):
 #   Column        Dtype  
---  ------        -----  
 0   GEOID         int64  
 1   Trip_Miles    float64
 2   Trip_Seconds  float64
dtypes: float64(2), int64(1)
memory usage: 777.8 MB


trip_info = year_data.groupby("GEOID").mean().reset_index()


trip_info


trip_info.to_csv("trip-info.csv")


pickups = year_data["GEOID"].value_counts().reset_index()


pickups.columns = ["GEOID","Pickups"]


pickups


import folium
import geopandas as gpd

geodata = gpd.read_file("shapes.geojson")

geodata = geodata[geodata["GEOID"].notna()]
geodata = geodata.astype({"GEOID":"int"}) 

geodata = geodata[geodata["GEOID"].isin(pickups["GEOID"])]
geodata = geodata.to_crs(epsg = 4326)


geodata.head()


pickup_map = folium.Map(location = [41.8,-87.6])
pickup_map


folium.Choropleth(
    geo_data=geodata,
    name="choropleth",
    data=pickups,
    columns=["GEOID","Pickups"],
    threshold_scale = [0,50000,100000,150000,200000,300000,500000,750000,1000000, 1200000],
    key_on="feature.properties.GEOID",
    fill_color="BuPu",
    fill_opacity=0.7,
    line_opacity=.1,
    legend_name="Pickups by Census Tract",
).add_to(pickup_map)

folium.LayerControl().add_to(pickup_map)


pickup_map


built_environment = pd.read_csv("built-environment-demographics.csv")


trip_info = pd.merge(trip_info, pickups)

# main_df = pd.merge(built_environment, trip_info)


main_df = pd.merge(trip_info, built_environment)


main_df


main_df = main_df.drop(columns=["Unnamed: 0","Unnamed: 0.1"])


main_df


main_df.columns

Index(['GEOID', 'Trip_Miles', 'Trip_Seconds', 'Pickups', 'MedianIncome',
       'Pickup', 'TotalPopulation', 'Population_Density',
       ' Employment_Density', 'Percent_Zero_Car_Ownership',
       'LandUse_Diversity', 'Distance_from_transit'],
      dtype='object')


main_df = main_df[['GEOID', 'Pickups','Trip_Miles', 'Trip_Seconds', 'MedianIncome',
        'TotalPopulation', 'Population_Density',
       ' Employment_Density', 'Percent_Zero_Car_Ownership',
       'LandUse_Diversity', 'Distance_from_transit']]


import seaborn as sns

X = main_df[main_df.columns[1:12]]

X


X = X.dropna()
X = X[(X != 0).all(1)]


X.shape

(889, 10)


sns.pairplot(X)

<seaborn.axisgrid.PairGrid at 0x7fc848d21f40>


sns.heatmap(X.corr())

<AxesSubplot:>


X.describe()


from sklearn.cluster import KMeans
import matplotlib.pyplot as plt

wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()


kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, n_init=10, random_state=0)
kmeans.fit(X)
clusters = kmeans.predict(X)


cluster_centers = pd.DataFrame(kmeans.cluster_centers_)
cluster_centers.columns = X.columns.values
cluster_centers


import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
import numpy as np
import pandas as pd
from scipy.cluster.hierarchy import dendrogram
from pandas.plotting import parallel_coordinates


palette = sns.color_palette("bright", 10)


def addAlpha(colour, alpha):
    '''Add an alpha to the RGB colour'''
    
    return (colour[0],colour[1],colour[2],alpha)


def display_factorial_planes(X_projected, n_comp, pca, axis_ranks, labels=None, alpha=1, illustrative_var=None):
    '''Display a scatter plot on a factorial plane, one for each factorial plane'''

    # For each factorial plane
    for d1,d2 in axis_ranks:
        if d2 < n_comp:
 
            # Initialise the matplotlib figure      
            fig = plt.figure(figsize=(7,6))
        
            # Display the points
            if illustrative_var is None:
                plt.scatter(X_projected[:, d1], X_projected[:, d2], alpha=alpha)
            else:
                illustrative_var = np.array(illustrative_var)
                for value in np.unique(illustrative_var):
                    selected = np.where(illustrative_var == value)
                    plt.scatter(X_projected[selected, d1], X_projected[selected, d2], alpha=alpha, label=value)
                plt.legend()

            # Display the labels on the points
            if labels is not None:
                for i,(x,y) in enumerate(X_projected[:,[d1,d2]]):
                    plt.text(x, y, labels[i],
                              fontsize='14', ha='center',va='center') 
                
            # Define the limits of the chart
            boundary = np.max(np.abs(X_projected[:, [d1,d2]])) * 1.1
            plt.xlim([-boundary,boundary])
            plt.ylim([-boundary,boundary])
        
            # Display grid lines
            plt.plot([-100, 100], [0, 0], color='grey', ls='--')
            plt.plot([0, 0], [-100, 100], color='grey', ls='--')

            # Label the axes, with the percentage of variance explained
            plt.xlabel('PC{} ({}%)'.format(d1+1, round(100*pca.explained_variance_ratio_[d1],1)))
            plt.ylabel('PC{} ({}%)'.format(d2+1, round(100*pca.explained_variance_ratio_[d2],1)))

            plt.title("Projection of points (on PC{} and PC{})".format(d1+1, d2+1))
            #plt.show(block=False)


def display_parallel_coordinates(df, num_clusters):
    '''Display a parallel coordinates plot for the clusters in df'''

    # Select data points for individual clusters
    cluster_points = []
    for i in range(num_clusters):
        cluster_points.append(df[df.cluster==i])
    
    # Create the plot
    fig = plt.figure(figsize=(12, 15))
    title = fig.suptitle("Parallel Coordinates Plot for the Clusters", fontsize=18)
    fig.subplots_adjust(top=0.95, wspace=0)

    # Display one plot for each cluster, with the lines for the main cluster appearing over the lines for the other clusters
    for i in range(num_clusters):    
        plt.subplot(num_clusters, 1, i+1)
        for j,c in enumerate(cluster_points): 
            if i!= j:
                pc = parallel_coordinates(c, 'cluster', color=[addAlpha(palette[j],0.2)])
        pc = parallel_coordinates(cluster_points[i], 'cluster', color=[addAlpha(palette[i],0.5)])

        # Stagger the axes
        ax=plt.gca()
        for tick in ax.xaxis.get_major_ticks()[1::2]:
            tick.set_pad(20)        


def display_parallel_coordinates_centroids(df, num_clusters):
    '''Display a parallel coordinates plot for the centroids in df'''

    # Create the plot
    fig = plt.figure(figsize=(12, 5))
    title = fig.suptitle("Parallel Coordinates plot for the Centroids", fontsize=18)
    fig.subplots_adjust(top=0.9, wspace=0)

    # Draw the chart
    parallel_coordinates(df, 'cluster', color=palette)

    # Stagger the axes
    ax=plt.gca()
    for tick in ax.xaxis.get_major_ticks()[1::2]:
        tick.set_pad(20)


from sklearn.preprocessing import StandardScaler

sc = StandardScaler()
sc_X = sc.fit_transform(X)
sc_X = pd.DataFrame(sc_X)


sc_X


import matplotlib.pyplot as plt
from matplotlib.collections import LineCollection
from scipy.cluster.hierarchy import dendrogram
from pandas.plotting import parallel_coordinates


X_scaled_clustered = pd.DataFrame(sc_X, columns=sc_X.columns, index=sc_X.index)
X_scaled_clustered['cluster'] = clusters

X_scaled_clustered.head()


from sklearn.decomposition import PCA

# Create a PCA model to reduce our data to 2 dimensions for visualisation
pca = PCA(n_components=2)
pca.fit(sc_X)

# Transfor the scaled data to the new PCA space
X_reduced = pca.transform(sc_X)


# Convert to a data frame
X_reduceddf = pd.DataFrame(X_reduced, index=X.index, columns=['PC1','PC2'])
X_reduceddf['cluster'] = clusters
X_reduceddf.head()


display_factorial_planes(X_reduced, 2, pca, [(0,1)], illustrative_var = clusters, alpha = 0.8)


# Add the cluster number to the original scaled data
X_clustered = pd.DataFrame(sc_X, index=sc_X.index, columns=sc_X.columns)
X_clustered["cluster"] = clusters

# Display parallel coordinates plots, one for each cluster
display_parallel_coordinates(X_clustered, 3)


# Create a data frame containing our centroids
centroids = pd.DataFrame(sc.fit_transform(kmeans.cluster_centers_), columns=X.columns)
centroids['cluster'] = centroids.index

display_parallel_coordinates_centroids(centroids, 10)


cluster_distribution = X_reduceddf["cluster"].value_counts().to_frame().reset_index()


cluster_distribution


cluster_distribution.columns = ["cluster","number"]


cluster_distribution


plt.pie(cluster_distribution["number"], labels = ["Cluster 0","Cluster 2","Cluster 1"], shadow=True)

([<matplotlib.patches.Wedge at 0x7fc8221529a0>,
  <matplotlib.patches.Wedge at 0x7fc8221631c0>,
  <matplotlib.patches.Wedge at 0x7fc822163910>],
 [Text(-0.8848651129616366, 0.653462877188437, 'Cluster 0'),
  Text(0.861225012024064, -0.6843182583156402, 'Cluster 2'),
  Text(1.0993132202631353, -0.0388644278832949, 'Cluster 1')])

	Trip ID	Trip Start Timestamp	Trip End Timestamp	Trip Seconds	Trip Miles	Pickup Census Tract	Dropoff Census Tract	Pickup Community Area	Dropoff Community Area	Fare	...	Additional Charges	Trip Total	Shared Trip Authorized	Trips Pooled	Pickup Centroid Latitude	Pickup Centroid Longitude	Pickup Centroid Location	Dropoff Centroid Latitude	Dropoff Centroid Longitude	Dropoff Centroid Location
0	c4a636bddc6b0cc53d20b2cc5635ff5f0b4a8141	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	721	3.8	NaN	1.703198e+10	NaN	76.0	15.0	...	7.85	27.85	False	1	NaN	NaN	NaN	41.979071	-87.903040	POINT (-87.9030396611 41.9790708201)
1	000f3eb1fc6020bfb8a7daf0441589d314aac546	01/01/2020 12:00:00 AM	01/01/2020 01:45:00 AM	6399	89.0	NaN	1.703123e+10	NaN	23.0	95.0	...	3.35	98.35	False	1	NaN	NaN	NaN	41.899062	-87.721316	POINT (-87.7213158985 41.8990616211)
2	8af67df7ca52185b15dc4fa29f2d972448fda731	01/01/2020 12:00:00 AM	01/01/2020 12:15:00 AM	1027	8.3	NaN	NaN	15.0	77.0	12.5	...	2.55	15.05	False	1	41.954028	-87.763399	POINT (-87.7633990316 41.9540276487)	41.986712	-87.663416	POINT (-87.6634164054 41.9867117999)
3	8be05cd4d7bf1997d306fc21dd4c37b9f9413558	01/01/2020 12:00:00 AM	01/01/2020 12:00:00 AM	384	2.0	NaN	NaN	28.0	28.0	5.0	...	2.55	10.55	False	1	41.874005	-87.663518	POINT (-87.6635175498 41.874005383)	41.874005	-87.663518	POINT (-87.6635175498 41.874005383)
4	8e7e77c3dd22422740402143aa88912521922ee2	01/01/2020 12:00:00 AM	01/01/2020 12:15:00 AM	553	2.4	NaN	NaN	9.0	10.0	7.5	...	2.55	13.05	False	1	42.007613	-87.813781	POINT (-87.8137810343 42.0076125931)	41.985015	-87.804532	POINT (-87.8045320063 41.9850151008)
5	003454de9b0cb5a3d5a1723d40438f07b574749d	01/01/2020 12:00:00 AM	01/01/2020 12:15:00 AM	884	4.4	1.703101e+10	1.703104e+10	1.0	4.0	10.0	...	2.55	13.55	False	1	42.015934	-87.666536	POINT (-87.6665362779 42.0159343756)	41.972563	-87.678846	POINT (-87.6788459662 41.9725625375)
6	9343d1e5f13822780e7f8df61463401b90a576ab	01/01/2020 12:00:00 AM	01/01/2020 12:30:00 AM	1134	4.7	NaN	NaN	30.0	30.0	10.0	...	2.55	12.55	False	1	41.839087	-87.714004	POINT (-87.714003807 41.8390869059)	41.839087	-87.714004	POINT (-87.714003807 41.8390869059)
7	94314539302831a4a82eb35789f22644b006201b	01/01/2020 12:00:00 AM	01/01/2020 12:45:00 AM	2559	18.9	NaN	NaN	20.0	43.0	17.5	...	2.55	20.05	True	2	41.924347	-87.734740	POINT (-87.7347397536 41.9243470769)	41.761578	-87.572782	POINT (-87.5727819867 41.7615779081)
8	005827e8adc1c35df898021c18fb9fd3e4f1f5cb	01/01/2020 12:00:00 AM	01/01/2020 12:15:00 AM	977	12.4	NaN	NaN	32.0	NaN	17.5	...	2.55	20.05	False	1	41.878866	-87.625192	POINT (-87.6251921424 41.8788655841)	NaN	NaN	NaN
9	005d727d45694054ff3502870af8826c255941a5	01/01/2020 12:00:00 AM	01/01/2020 12:15:00 AM	1205	11.6	NaN	NaN	69.0	27.0	17.5	...	2.55	20.05	False	1	41.763247	-87.616134	POINT (-87.6161341112 41.7632467988)	41.878914	-87.705897	POINT (-87.7058971305 41.8789144956)

	Trip Seconds	Trip Miles	Pickup Census Tract
0	721.0	3.8	NaN
1	6399.0	89.0	NaN
2	1027.0	8.3	NaN
3	384.0	2.0	NaN
4	553.0	2.4	NaN
5	884.0	4.4	1.703101e+10
6	1134.0	4.7	NaN
7	2559.0	18.9	NaN
8	977.0	12.4	NaN
9	1205.0	11.6	NaN

	Trip_Seconds	Trip_Miles	GEOID
5	884.0	4.4	1.703101e+10
10	759.0	4.0	1.703183e+10
11	248.0	1.3	1.703106e+10
12	276.0	1.1	1.703183e+10
14	513.0	2.8	1.703103e+10
...	...	...	...
49871368	1258.0	6.5	1.703103e+10
49871369	919.0	4.6	1.703183e+10
49871370	454.0	1.6	1.703107e+10
49871382	575.0	2.2	1.703108e+10
49871384	788.0	3.7	1.703107e+10

	Trip_Seconds	Trip_Miles	GEOID
5	884.0	4.4	17031010300
10	759.0	4.0	17031832600
11	248.0	1.3	17031063302
12	276.0	1.1	17031832200
14	513.0	2.8	17031030701

	GEOID	Trip_Miles	Trip_Seconds
5	17031010300	4.4	884.0
10	17031832600	4.0	759.0
11	17031063302	1.3	248.0
12	17031832200	1.1	276.0
14	17031030701	2.8	513.0

	GEOID	Trip_Miles	Trip_Seconds
0	17031010100	7.179774	1180.483869
1	17031010201	6.450781	1080.175410
2	17031010202	4.742800	882.345516
3	17031010300	6.869025	1144.760704
4	17031010400	7.030327	1185.853686
...	...	...	...
1135	17031843700	5.527061	1057.733495
1136	17031843800	7.151539	1058.850033
1137	17031843900	7.479913	1096.066109
1138	17031980000	19.692362	1904.696142
1139	17031980100	16.828538	1884.554266

	GEOID	Pickups
0	17031839100	1163394
1	17031980000	1046728
2	17031320100	746423
3	17031833000	739580
4	17031081700	712078
...	...	...
1135	17031826902	1
1136	17031822000	1
1137	17031801608	1
1138	17031823605	1
1139	17031823903	1

	STATEFP	COUNTYFP	TRACTCE	AFFGEOID	GEOID	NAME	LSAD	ALAND	geometry
0	17	031	843800	1400000US17031843800	17031843800	8438	CT	1309516	POLYGON ((-87.64554 41.80886, -87.64068 41.808...
2	17	031	243000	1400000US17031243000	17031243000	2430	CT	324548	POLYGON ((-87.68195 41.89583, -87.67950 41.895...
3	17	031	250600	1400000US17031250600	17031250600	2506	CT	647765	POLYGON ((-87.77560 41.90925, -87.77536 41.909...
4	17	031	251700	1400000US17031251700	17031251700	2517	CT	486655	POLYGON ((-87.74826 41.89498, -87.74645 41.895...
5	17	031	260400	1400000US17031260400	17031260400	2604	CT	328225	POLYGON ((-87.74061 41.88781, -87.73571 41.887...

	Pickups	Trip_Miles	Trip_Seconds	MedianIncome	TotalPopulation	Population_Density	Employment_Density	Percent_Zero_Car_Ownership	LandUse_Diversity	Distance_from_transit
0	28499	6.450781	1080.175410	41125	7039	59.119022	1.490412	0.019724	0.069242	234.2500
1	23203	4.742800	882.345516	45236	2852	36.278590	8.568341	0.027317	0.724295	193.7950
2	28471	6.869025	1144.760704	72917	6650	58.470922	9.582295	0.106673	0.346823	184.4025
3	32895	7.030327	1185.853686	58438	5153	64.711477	29.071885	0.023679	2.464812	152.4400
4	19174	7.247471	1185.797851	52747	4147	84.888695	3.081571	0.032108	0.066411	113.1000
...	...	...	...	...	...	...	...	...	...	...
890	4929	8.583506	1297.040576	28750	10317	23.140485	6.286981	0.085057	9.501695	537.7900
891	22670	5.911914	956.318086	67609	2918	28.806947	3.053905	0.114072	0.230467	264.8700
892	46532	5.527061	1057.733495	194375	2617	14.264697	16.557479	0.100851	4.656542	283.6450
893	1527	7.151539	1058.850033	41250	1482	4.633875	0.716691	0.092342	0.372437	274.9300
894	20451	7.479913	1096.066109	50104	3521	35.433393	2.294889	0.098458	0.380601	199.0220

	Pickups	Trip_Miles	Trip_Seconds	MedianIncome	TotalPopulation	Population_Density	Employment_Density	Percent_Zero_Car_Ownership	LandUse_Diversity	Distance_from_transit
count	8.890000e+02	889.000000	889.000000	889.000000	889.000000	889.000000	889.000000	889.000000	889.000000	889.000000
mean	2.698930e+04	6.896870	1098.841019	84220.039370	3679.753656	31.174132	11.469629	0.208842	1.535303	279.243646
std	8.115247e+04	2.642166	230.613680	54337.144703	1832.146235	27.338535	49.842658	0.156357	7.612212	112.575014
min	2.000000e+00	3.322565	688.884793	11457.000000	416.000000	2.139127	0.027023	0.002972	0.004566	23.470000
25%	1.532000e+03	5.378845	954.960748	44911.000000	2279.000000	15.517109	1.572308	0.076892	0.184376	211.900000
50%	5.553000e+03	6.527606	1065.800529	67000.000000	3485.000000	25.432728	3.351064	0.172012	0.401153	257.493333
75%	1.846000e+04	7.515382	1194.269912	107267.000000	4861.000000	38.537783	7.697686	0.304828	0.950401	327.900000
max	1.163394e+06	37.400000	2655.400000	250000.000000	19889.000000	407.584189	1102.545869	0.740122	176.285714	1062.170000

	Pickups	Trip_Miles	Trip_Seconds	MedianIncome	TotalPopulation	Population_Density	Employment_Density	Percent_Zero_Car_Ownership	LandUse_Diversity	Distance_from_transit
0	8348.449929	7.248445	1131.588034	61194.454161	3672.187588	27.764839	4.392629	0.207883	0.936670	285.514218
1	644577.600000	4.490997	805.811809	185783.900000	8360.400000	72.685729	297.063445	0.339215	14.438450	173.048917
2	68403.870588	5.572115	979.503715	174275.929412	3435.976471	42.951031	24.185244	0.205175	3.272948	259.338417

	0	1	2	3	4	5	6	7	8	9
0	0.018614	-0.168930	-0.080984	-0.793551	1.834535	1.022755	-0.200327	-1.210209	-0.192702	-0.399902
1	-0.046683	-0.815725	-0.939308	-0.717851	-0.452049	0.186818	-0.058242	-1.161620	-0.106600	-0.759465
2	0.018269	-0.010545	0.199232	-0.208134	1.622097	0.999035	-0.037887	-0.653805	-0.156216	-0.842945
3	0.072814	0.050539	0.377522	-0.474750	0.804562	1.227433	0.353355	-1.184900	0.122176	-1.127026
4	-0.096358	0.132769	0.377279	-0.579544	0.255170	1.965899	-0.168385	-1.130961	-0.193074	-1.476679
...	...	...	...	...	...	...	...	...	...	...
884	-0.271991	0.638713	0.859928	-1.021424	3.624701	-0.294023	-0.104039	-0.792128	1.047117	2.297951
885	-0.053254	-0.372993	-0.618364	-0.305875	-0.416005	-0.086637	-0.168941	-0.606457	-0.171510	-0.127752
886	0.240950	-0.518734	-0.178353	2.028391	-0.580386	-0.618868	0.102136	-0.691061	0.410261	0.039119
887	-0.313935	0.096441	-0.173509	-0.791249	-1.200227	-0.971347	-0.215859	-0.745509	-0.152849	-0.038340
888	-0.080613	0.220793	-0.012039	-0.628212	-0.086698	0.155885	-0.184178	-0.706372	-0.151776	-0.713007

	PC1	PC2
0	-0.057504	0.243946
1	0.430891	-1.094114
2	0.193360	0.337670
3	0.296173	0.362192
4	0.250216	-0.355774

	index	cluster
0	0	709
1	2	170
2	1	10

	cluster	number
0	0	709
1	2	170
2	1	10